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In this paper, we introduce a new topology of Fuzzy Polynomial Neural Networks (FPNN) that is based on a genetically optimized multi-layer perceptron with fuzzy polynomial neurons (FPNs) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially genetic algorithms (GAs). The development of the ‘conventional’ FPNNs uses an extended Group Method of Data Handling. The network exploits a fixed fuzzy inference type in each FPN of the FPNN as well as considers a fixed number of input nodes at FPNs (or nodes) located in each layer. Here, the proposed FPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional FPNNs. The structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least-square method-based learning. The performance of the networks is quantified through experimentation involving two time series dataset already used in fuzzy modeling. The results demonstrate their superiority over the existing fuzzy and neural models. [Copyright &y& Elsevier]

This paper proposes an identification method for nonlinear models realized in the form of implicit rule-based fuzzy-neural networks (FNN). The design of the model dwells on the technologies of computational intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithm. The FNN modeling and identification environment realizes parameter estimation through a synergistic usage of clustering techniques, genetic optimization and a complex search method. An HCM (Hard C-Means) clustering algorithm helps determine an initial location (parameters) of the membership functions of the information granules to be used in this fuzzy model. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are then adjusted using the optimization algorithm of a GA hybrid scheme. The proposed GA hybrid scheme combines GA with the improved complex method to guarantee both global optimization and local convergence. An aggregate objective function (performance index) is used in the model design in order to achieve a sound balance between its approximation and generalization abilities. The proposed type of the model is experimented with several time series data (gas furnace, sewage treatment process, and NOx emission process data of gas turbine power plant). [Copyright &y& Elsevier]

• AFS and RS are used to effectively obtain the transmittance spectra of black plastic wastes not available in the existing NIR. • A feature selection method of the peak points is considered based on the analysis of chemical information for low cost spectrometer by using a small amount of input variables. • A method of 2D reconstruction through mapping from 1D signal data based on peak points is proposed to effectively deal with spatial structure information between data. • 2DCNN design driven by 2D information features or chemical peak points data leads to high performance as well as anti-noise capability through effective data mapping. In this study, two types of convolutional neural network (CNN) classifiers are designed to handle the problem of classifying black plastic wastes. In particular, the black plastic wastes have the property of absorbing laser light coming from spectrometer. Therefore, the classification of black plastic wastes remains still a challenging problem compared to classifying other colored plastic wastes using existing spectroscopy (i.e., NIR). When it comes the classification problem of black plastic wastes, effective classification techniques by the laser spectroscopy of Fourier Transform-Infrared Radiation (FT-IR) with Attenuated Total Reflectance (ATR) and Raman to analyze the classification problem of black plastic wastes are introduced. Due to the strong ability of extracting spatial features and remarkable performance in image classification, 1D and 2D CNN through data features are designed as classifiers. The technique of chemical peak points selection is considered to reduce data redundancy. Furthermore, through the selection of data features based on the extracted 1D data with peak points is introduced. Experimental results demonstrate that 2DCNN classifier designed with the help of 2D data feature selection as well as 1DCNN classifier shows the best performance compared with other reported methods for classifying black plastic wastes. [ABSTRACT FROM AUTHOR]

The digital twin (DT) represents a powerful tool for advancing construction industry to provide a cyber–physical integration that enables real-time monitoring of assets and activities and facilitates decision-making. Due to the inherent characteristics of the construction industry and the diverse possibilities with DT, proliferation of building digital twin (BDT) necessitates a comprehensive comprehension of its evolution and the creation of roadmaps. This paper aims to contribute to the formalization and standardization of BDT. It designs a novel assessment framework for the overall maturity measurement of existing BDT projects. The developed BDT maturity model incorporates a collective opinion generation paradigm based on a fairness-aware multiobjective optimization model to provide an expert-based evaluation system for evaluating the maturity of BDT projects. The effectiveness and feasibility of the proposed framework have been validated through a case study of an experimental BDT initiative. This paper establishes a generalizable framework for BDT maturity assessment that can offer insights into BDT maturity standards to construction practitioners to create effective strategies for the diffusion, development, and maturation of BDT. [ABSTRACT FROM AUTHOR]